@inproceedings{hossain-2026-rms,
    title = "{RMS}@{D}ravidian{L}ang{T}ech 2026: Multimodal Gated Fusion for Hierarchical {T}amil Political Meme Classification",
    author = "Hossain, Md. Ajwad",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.53/",
    pages = "341--347",
    ISBN = "979-8-89176-401-9",
    abstract = "Internet memes have become a dominant and highly accessible medium for political discourse on social media. However, their multimodal nature{---}combining culturally specific visual symbols with code-mixed text{---}presents a significant challenge for automated content analysis, particularly in low-resource languages. In this study, we describe the system submitted by team RMS for the Multi-Level Political Meme Classification shared task at DravidianLangTech @ ACL 2026, focusing exclusively on the Tamil language track. We propose a robust late-fusion multimodal architecture that leverages a pre-trained ResNet-50 network for visual feature extraction and a Transformer-based model (MuRIL) for processing code-mixed Tamil text. The modalities are aligned using bidirectional cross-modal attention and combined using a Gated Multimodal Unit, allowing the model to dynamically weight the importance of visual versus textual cues. Our system ranked 11th on the official leaderboard with a macro-averaged F1-score of 0.7382. Through detailed error analysis, we demonstrate that while our gated fusion approach excels at identifying explicit trolling stances, it struggles with complex target resolution when visual and textual cues contradict."
}